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FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling
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FoME: A Foundation Model for EEG using Adaptive Temporal-Lateral Attention Scaling
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Electroencephalography (EEG) is a vital tool to measure and record brain activity in neuroscience and clinical applications, yet its potential is constrained by signal heterogeneity, low signal-to-noise ratios, and limited labeled datasets. In this paper, we propose FoME (Foundation Model for EEG), a novel approach using adaptive temporal-lateral attention scaling to address above-mentioned challenges. FoME is pre-trained on a diverse 1.7TB dataset of scalp and intracranial EEG recordings, comprising 745M parameters trained for 1,096k steps. Our model introduces two key innovations: a time-frequency fusion embedding technique and an adaptive time-lateral attention scaling (ATLAS) mechanism. These components synergistically capture complex temporal and spectral EEG dynamics, enabling FoME to adapt to varying patterns across diverse data streams and facilitate robust multi-channel modeling. Evaluations across four downstream tasks demonstrate FoME's superior performance in classification and forecasting applications, consistently achieving state-of-the-art results. To conclude, FoME establishes a new paradigm for EEG analysis, offering a versatile foundation that advances brain-computer interfaces, clinical diagnostics, and cognitive research across neuroscience and related fields. Our code will be available at https://github.com/1061413241/FoME.
Forward citations
Cited by 5 Pith papers
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NeuroAtlas benchmarks foundation models on 42 EEG datasets and reports that EEG-specific models do not consistently outperform generic time-series models, standard metrics miss clinical utility, and rankings vary by domain.
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A unified benchmark of 12 EEG foundation models across 13 datasets finds specialists remain competitive and larger pre-trained models do not consistently improve downstream decoding.
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Large-Scale AI and Foundation Models for Neuroscience: A Comprehensive Review
This paper is a survey: it organizes existing foundation-model work in neuroscience into five application domains and lists public datasets, without presenting new experiments.
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